W2D

This repo is the official implementation of our CVPR 2022 paper "The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization".

Getting Started

Data Preparation

• Downloader datasets (except NICO and CelebA datasets)

python3 -m domainbed.scripts.download \
       --data_dir=./domainbed/data

• Download CelebA dataset from here
• Download clean NICO dataset (provided by ours) from here
• The directory structures are discribed at OoD-Bench

Install

• Pytorch

Launch a sweep

cd /ood_bench/DomainBed
bash sweep/"dataset_name"/run.sh launch ../datasets 0

• To change the training setting, modify the scripts under /ood_bench/DomainBed/sweep.
• If you have any questions about the scripts, more details are discribed at OoD-Bench and DomainBed.
• Note: Since ResNet is not used in Colored_MNIST dataset, when you train on Colored_MNIST, uncomment line 992-1020 at algorithms.py.

View the results

python -m domainbed.scripts.collect_results\
       --input_dir="sweep_output_path"

Acknowledgments

The codebase is built upon OoD-Bench and DomainBed.